Supervised Learning of Fuzzy ARTMAP Neural Networks Through Particle Swarm Optimization

In this paper, the impact on fuzzy ARTMAP performance of decisions taken for batch supervised learning is assessed through computer simulation. By learning different real-world and synthetic data, using different learning strategies, training set sizes, and hyper-parameter values, the generalization error and resources requirements of this neural network are compared. In particular, the degradation of fuzzy ARTMAP performance due to overtraining is shown to depend on factors such as the training set size and the number of training epochs, and occur for pattern recognition problems in which class distributions overlap. Although the hold-out learning strategy is commonly employed to avoid overtraining, results indicate that it is not necessarily justified. As an alternative, a new Particle Swarm Optimization (PSO) learning strategy, based on the concept of neural network evolution, has been introduced. It co-jointly determines the weights, architecture and hyper-parameters such that generalization error is minimized. Through a comprehensive set of simulations, it has been shown that when fuzzy ARTMAP uses this strategy, it produces a significantly lower generalization error, and mitigates the degradation of error due to overtraining. Overall, the results reveal the importance of optimizing all fuzzy ARTMAP parameters for a given problem, using a consistent objective function.